Creating and running indexers in Azure Cognitive Search is a fundamental component of implementing knowledge mining and information extraction solutions. An indexer is an automated crawler that extracts searchable content from various data sources and populates a search index.
To create an indexer…Creating and running indexers in Azure Cognitive Search is a fundamental component of implementing knowledge mining and information extraction solutions. An indexer is an automated crawler that extracts searchable content from various data sources and populates a search index.
To create an indexer, you first need to establish three key components: a data source connection, a target search index, and optionally a skillset for AI enrichment. The data source defines where your content resides, such as Azure Blob Storage, Azure SQL Database, Cosmos DB, or Azure Table Storage.
You can create indexers through multiple methods: the Azure portal, REST API, or Azure SDKs (.NET, Python, Java). When using the REST API, you submit a POST request to the indexers endpoint with a JSON definition specifying the data source name, target index name, and scheduling parameters.
Key configuration options include field mappings, which define how source fields map to index fields, and output field mappings for skillset-enriched content. You can also configure change detection policies to enable incremental indexing, processing only new or modified documents.
Running indexers can be done on-demand or scheduled. On-demand execution triggers a single indexing run, useful for testing or initial data loads. Scheduled execution allows indexers to run at defined intervals, keeping your index synchronized with source data changes.
Monitoring indexer status is essential for maintaining healthy search solutions. Azure provides execution history, showing success counts, failure details, and processing times. You can track document-level errors and warnings through the indexer status API.
Best practices include setting appropriate batch sizes based on document complexity, implementing retry policies for transient failures, and using reset operations when reprocessing is necessary. For large datasets, consider partitioning strategies and parallel indexer runs to optimize throughput and ensure efficient knowledge extraction from your data sources.
Creating and Running Indexers in Azure AI Search
Why It Is Important
Indexers are fundamental components in Azure AI Search that automate the process of extracting data from various sources and populating your search index. Understanding how to create and run indexers is essential for the AI-102 exam because they form the backbone of knowledge mining solutions, enabling you to build intelligent search experiences over large datasets efficiently.
What Are Indexers?
An indexer in Azure AI Search is an automated crawler that: - Connects to external data sources (Azure Blob Storage, Azure SQL Database, Cosmos DB, Azure Table Storage) - Extracts searchable content and metadata - Serializes documents into JSON format - Populates a search index with the extracted data
Indexers eliminate the need for manual code to push data into your index, making data ingestion streamlined and maintainable.
How Indexers Work
Key Components: 1. Data Source: Defines the connection to your external data repository 2. Index: The target schema where documents will be stored 3. Skillset (optional): AI enrichment pipeline for cognitive processing 4. Indexer: The orchestrator that connects data source to index
Execution Process: - Indexer connects to the specified data source - Retrieves documents based on configured parameters - Applies field mappings to transform source fields to index fields - Optionally applies skillset for AI enrichment - Pushes processed documents to the search index
Creating an Indexer
Indexers can be created using: - Azure Portal (Import Data wizard) - REST API (POST to /indexers endpoint) - Azure SDK (.NET, Python, JavaScript)
Essential Configuration Properties: - name: Unique identifier for the indexer - dataSourceName: Reference to the data source - targetIndexName: Reference to the destination index - skillsetName: Reference to cognitive skillset (if using AI enrichment) - schedule: Defines recurring execution intervals - fieldMappings: Maps source fields to index fields - outputFieldMappings: Maps enriched content to index fields
Running Indexers
Execution Options: - On-demand: Manual trigger via portal or API - Scheduled: Automatic execution at defined intervals (minimum 5 minutes) - Change detection: Incremental indexing based on high-water marks
Monitoring: - Check indexer status (running, success, error) - Review execution history - Examine document counts and error logs
Field Mappings
Field mappings handle scenarios where source field names differ from index field names: - sourceFieldName: The field in the source document - targetFieldName: The corresponding field in the index - mappingFunction: Optional transformation (base64Encode, extractTokenAtPosition, etc.)
Exam Tips: Answering Questions on Creating and Running Indexers
1. Remember the dependency chain: Data Source → Index → Skillset → Indexer. The indexer references all other components.
2. Know scheduling limits: The minimum schedule interval is 5 minutes. Questions may test this specific value.
3. Distinguish field mappings from output field mappings: Field mappings are for source-to-index mapping; output field mappings are for skillset enrichments to index fields.
4. Understand incremental indexing: Know that high-water mark columns and soft delete policies enable efficient change tracking.
5. Recognize supported data sources: Azure Blob Storage, SQL Database, Cosmos DB, and Table Storage are commonly tested options.
6. Pay attention to error handling: Know that maxFailedItems and maxFailedItemsPerBatch parameters control failure tolerance.
7. REST API endpoints: Remember that indexers use /indexers endpoint, and running an indexer uses /indexers/{name}/run.
8. When questions mention automated data refresh: The answer typically involves configuring an indexer schedule rather than manual processes.